Flux analysis explained: definition, examples, and use cases

Many accounting tasks are repeated over the business's financial cycles. Over time, these repeated reports and analyses reveal fluctuations. A fluctuation analysis, or flux analysis, is a way to identify, quantify, and qualify these variations over time. These analyses aid in identifying errors, patterns, and even drivers across reporting periods.

What is flux analysis?

Flux analysis, or fluctuation variance analysis, is an accounting tool for identifying and investigating changes between reporting periods. This is not to be confused with variance analysis, which is the comparison of a project's actual budget against its expected budget. Fluctuation analysis is a kind of horizontal analysis (or trend analysis) that compares the final values of specific indicators over two or more accounting periods.

Why is fluctuation analysis important?

Fluctuation analysis is crucial for identifying the reasons behind these variations over time. Regular flux analysis can:

• Improve financial data clarity
• Identify patterns and associated trends
• Describe and explain anomalies causing spikes or drops
• Manage risk by analyzing and defining variability
• Inform decision-making and optimize performance

This then aids in the development of more robust strategies and forecasts that can better adapt to the uncertainties and changes in complex financial environments.

When is flux analysis performed?

A flux analysis is done at the end of a financial reporting period, generally either annually or quarterly, as determined by your organization’s needs. No matter your chosen cadence, flux analysis must be performed with like periods: year to year, quarter to quarter, or month to month. When performed regularly over multiple cycles, these analyses create a trend line.

What are the common types of variance in flux analysis?

Receita

Revenue variations are the changes in sales as compared to data from past reports or sales projections for the period. Investigating and understanding the causes behind revenue variations improves budgeting and forecasts. If the actual is greater than the benchmark, the interpretation is positive.

Despesa

Expense variance represents changes in costs. Careful analysis is crucial here, since the impacts of these changes aren’t always straightforward. For example, a decrease in material cost could result in a decrease in the cost of goods sold, which would result in a gain in profit margin for the organization. But a decrease in expenses related to these same materials without a decrease in cost could be reflective of insufficient supplies, leading to lower sales when you run out.

Labor

In short, variance for payroll activities. These variations can have diverse causes, so careful and informed analysis is needed. Generally, if the actual is greater than the benchmark that is interpreted as negative.

Material

This is an expense cost variance specific to materials. This will include direct and indirect costs of materials. The cost of the raw material would be direct. Equipment or tools that facilitate the management of the material, such as transportation or safety equipment, would be an indirect cost. If the actual is greater than the benchmark, it is most often interpreted as negative, however in some cases, this may not be true, so a clear understanding of the causes behind these variations is necessary.

Despesas gerais

Overhead variance is the change of indirect costs of the business, such as utilities, accounting or legal fees, or office supplies to name a few. If the actual is greater than the benchmark, it is negative.

What are some common reasons for variances in flux analysis?

Variations detected by flux analysis will have one of two sources: errors in the data or real changes. Variance due to errors, though valuable for improving reporting, processes, and analysis, are more straightforward to resolve. Variance that reflects real changes relative to benchmarks requires more careful consideration to properly interpret and determine the causes and effects on the business. Some of these variances may overlap, so careful consideration of the specific fluctuation is crucial to properly defining these changes over time.

Error variances

Errors can be caused by incomplete data, errors in data entry, or general ledger formula mistakes. These errors cause the company's financial data to be inaccurate and misleading. Performing fluctuation analysis will identify these errors, and though they do not reflect real data, they present opportunities to improve accounting or reporting processes.

Volume variances

When a core part of a transaction changes, it results in a volume variance. For example, a sales volume variance will be found if there is a difference between the expected number of sales and the actual number of sales.

Price variances

Identifying price variance is a key component of managing costs. A price variance happens when the expected cost of a purchase is different from its expected cost. A lower actual cost than the expected or budgeted costs is always desirable.

Efficiency variances

Efficiency variance is a change in resource usage compared to expected usage. This could be raw materials, purchased parts, labor, or machine use. This can also be applied to services and utilities in some cases, such as time invested in an audit, or amount of fuel or electricity use.

How to conduct a flux analysis in 4 steps

1. Gather data about the item or balance to be analyzed

Collect all relevant data concerning the item or account you want to analyze. Collect and organize the statements and data, and ensure it is accurate and complete. Inaccurate and incomplete data will result in errors.

2. Define the parameters of the analysis

Clearly define the scope of the analysis. Determine time periods that will be analyzed, and the variables that will be tracked. Clearly defined boundaries and purpose for the analysis will keep the results clear, making them more useful.

3. Find the period-to-period difference in values

Calculate the difference in values between consecutive periods. Over time, these variations will highlight trends through the changes over time. To find the percentage change, use this formula:

% change: (Value in current period - Value in prior period) / Value in prior period

Quantifying these changes with a standardized method provides consistent comparison over time.

4. Explain any significant variance

Analyze and explain any significant variances you identify. This may require investigating the underlying causes to properly assess the impact. When discussing materiality thresholds, refer to the guidelines outlined in this SEC article if your organization is based in the United States. Understanding materiality helps determine which variances are significant enough to warrant further investigation and reporting.

Flux analysis example

Let's take a quick look at the fictional monthly sales revenue of a retail store over the first quarter of the year.

To identify significant changes and underlying causes, we first calculate the period-to-period differences. From January to February, the sales revenue increased by $5,000, resulting in a 10% change:

(55,000 - 50,000) / 50,000 = 0.1 or 10%

From February to March, the revenue increased by $15,000, leading to a 27.27% change:

(70,000 - 55,000) /55,000 = 0.2727 or 27.27%

The 10% increase in February may be due to a successful marketing campaign, while the 27.27% increase in March could be attributed to a new product launch and seasonal demand. These variances would require further investigation to identify the causes of these increases. Insights gained from this analysis could confirm the effectiveness of specific tactics used in a product launch or establish a causal link between seasonality and an increase in sales.

Benefits of flux analysis

Consistent fluctuation analysis has several benefits for the company, beyond simply accounting for variance in spending.

• Identifying trends and patterns: Regular fluctuation analysis helps identify trends and patterns over time, which improves forecasting accuracy and informs strategic planning.
• Risk management: By analyzing fluctuations, businesses can identify volatile risks and areas, and adapt their strategy accordingly to mitigate those risks.
• Performance optimization: The feedback these analyses provide highlights inefficiencies and possible areas of improvement, streamlining overall operational performance.
• Informed decision-making: Detailed insights into the dynamics of a system support data-driven decision-making.
• Understanding variability: Provides concrete reasoning behind variability, improving understanding and control of processes.
• Resource allocation: Guides optimal allocation of resources based on the analysis of past and present data.

Challenges of flux analysis

These analyses can become quite abstract, challenging teams to draw strong relationships between business activities and performance and raw data. Let's look at some common challenges teams might encounter.

• Lack of business understanding or acumen: Poor understanding of the business context prevents analysts and leadership from properly interpreting the results. Improper interpretations can lead to misguided strategic decisions. Deep subject knowledge is essential to correctly identify relevant variables, understand their impact, and adapt business plans accordingly.
• Requires data that might not be accessible or reliable: Effective flux analysis depends on regular access to high-quality data. In many cases, necessary data may be difficult to obtain or may suffer from inaccuracies, leading to unreliable analysis results.
• Unclear policies lead to inconsistency in reporting: Without clear policies and defined materiality thresholds, there will be inconsistencies in when and how flux analysis is performed and reported. The resulting irregular reporting and unreliable insights undermine the analysis' effectiveness and usefulness.

Flux analysis vs. vertical analysis

Flux analysis is a method of tracking the financial changes of a single company over several reporting cycles. It helps companies understand the causes and effects of these variances. This in turn improves forecasting, planning, strategy, and risk management. It requires accurate, consistent data over multiple periods and can be complex, demanding deep business knowledge for proper interpretation.

Vertical analysis examines financial statements within a single period and expresses each item as a percentage of a base figure. This presents each account as a percentage figure, like flux analysis, but instead of tracking changes over time, vertical analysis compares these separate accounts against each other. This can compare two accounts in the same company or can be used to compare two companies against each other. This straightforward method is useful for benchmarking against industry standards or competitors. Vertical analysis only provides a snapshot of one period, making it less useful for identifying trends.

While flux analysis focuses on changes over time, vertical analysis focuses on financial structure within a single period. Both kinds of analysis are valuable in their own ways, offering complementary insights that enhance understanding of a company’s financial performance and health.

Conclusion: Automate flux analysis with Prophix One™

Fluctuation analysis is a challenging task, especially when reliable data is unavailable. Prophix One centralizes your financial data and keeps it up to date and ensures that no errors impact your analysis. Fluctuation variance analysis can be easily added to your financial cycles, providing deeper insights and identifying trends fast and accurately. Curious to see how this works? Get a demo here.